Mixed modality recursive least squares and radial basis function-based scheme for transit time estimation in multiphase flow

1995 ◽  
Vol 31 (3) ◽  
pp. 179-180 ◽  
Author(s):  
J.A. Chambers
Keyword(s):  
Multiphase Flow ◽  
Radial Basis Function ◽  
Least Squares ◽  
Transit Time ◽  
Basis Function ◽  
Time Estimation ◽  
Recursive Least Squares ◽  
Radial Basis ◽  
Mixed Modality

A Hybrid Method Using Response Surface and Pattern Search for Design Optimization

2005 ◽  
Author(s):  
T. Zhang ◽  
K. K. Choi ◽  
S. Rahman
Keyword(s):  
Radial Basis Function ◽  
Least Squares ◽  
Response Surface ◽  
Basis Function ◽  
Function Approximation ◽  
Least Squares Method ◽  
Order Approximation ◽  
Pattern Search ◽  
Moving Least Squares ◽  
Radial Basis

This paper presents a new method to construct response surface function and a new hybrid optimization method. For the response surface function, the radial basis function is used for a zeroth-order approximation, while new bases is proposed for the moving least squares method for a first-order approximation. For the new hybrid optimization method, the gradient-based algorithm and pattern search algorithm are integrated for robust and efficient optimization process. These methods are based on: (1) multi-point approximations of the objective and constraint functions; (2) a multi-quadric radial basis function for the zeroth-order function representation or radial basis function plus polynomial based moving least squares approximation for the first-order function approximation; and (3) a pattern search algorithm to impose a descent condition. Several numerical examples are presented to illustrate the accuracy and computational efficiency of the proposed method for both function approximation and design optimization. The examples for function approximation indicate that the multi-quadric radial basis function and the proposed radial basis function plus polynomial based moving least squares method can yield accurate estimates of arbitrary multivariate functions. Results also show that the hybrid method developed provides efficient and convergent solutions to both mathematical and structural optimization problems.

scholarly journals Classificação do Risco de Quedas em Idosos com Least Squares Support Vector Regression Utilizando Sinais Eletromiográficos e Dinamométricos

2021 ◽  
Author(s):  
Daniele Alves Silva ◽  
Laiana Sepúlveda de Andrade Mesquita ◽  
Luan Marinho Morais Pereira ◽  
Nayra Ferreira Lima Castelo Branco ◽  
Hermes Manoel Galvão Castelo Branco ◽  
...  
Keyword(s):  
Radial Basis Function ◽  
Least Squares ◽  
Support Vector Regression ◽  
Basis Function ◽  
Support Vector ◽  
Radial Basis ◽  
Leave One Out

A determinação do risco de cair é de suma importância na assistência à saúde do idoso, pois a ocorrência de quedas nessa população trazem consequências em vários aspectos. Ferramentas de aprendizado de máquinas têm sido cada vez mais empregadas com este fim. Portanto, o objetivo deste estudo foi investigar a viabilidade da utilização de sinais eletromiográficos e dinamométricos na classificação do risco de quedas em idosos via modelo least squares support vector regression (LSSVR). Trinta e um voluntários idosos foram avaliados com a Escala de Equilíbrio de Berg (EEB), eletromiografia e dinamometria do membro inferior dominante. Para o modelo LSSVR foram utilizados kernels do tipo linear, polinomial e radial basis function (RBF), além de validações cruzadas pelos métodos leave one out e K-fold. Ambos os sinais apresentaram erros médios baixos na maioria das execuções realizadas. Dessa forma, verificou-se que é possível classificar o risco de quedas em idosos por meio de sinais eletromiográficos e dinamométricos aplicados ao modelo LSSVR.

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